Information and Knowledge Economies Work and Management in the Canadian Federal Public Service
Bibliographic record
Abstract
This research presents a critique of interpretations and management of information and knowledge as operative in the architecture of our modem global economy. Given the transformation of this economic infrastructure, we state that there is a concomitant need to examine and clarify the operative logic of expert systems and networks of knowledge. The case example we use is the nature of the knowledge-based economy as it appears in the Canadian Federal Civil Services. Archival research and interviews with a range of Federal Government Departments and Agencies on a number of topics including employment management practices and knowledge management were conducted. The archival research reveals profound yet articulated changes in the infrastructure of the work force. It became clear that there is a concomitant but disturbingly un articulated change in the processes involving the operations of information and knowledge. We distinguish and contrast these with definitions derived from semiotic and information science frameworks. We argue for the importance of the collective and processual nature of knowledge. Our conclusions allow us to specify the shortcomings of existing knowledge management approaches and to identify a necessary and specific focus for future knowledge initiatives in organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.048 | 0.019 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".